自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

                  End-to-End Text Recognition with Convolutional Neural Networks

                  NLPIR SEMINAR Y2019#5

                  INTRO

                  In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.

                  Arrangement

                  This week’s seminar is organized as follows:

                  1. The seminar time is 1.pm, Mon, at Zhongguancun Technology Park ,Building 5, 1306.
                  2. The lecturer is Wang Gang, the paper’s title is End-to-End Text Recognition with Convolutional Neural Networks.
                  3. The seminar will be hosted by Qinghong Jiang.
                  4. Attachment is the paper of this seminar, please download in advance.

                  Everyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper.

                  End-to-End Text Recognition with Convolutional Neural Networks

                  Tao Wang   David J. Wu        Adam Coates       Andrew Y. Ng

                  Abstract

                         Full end-to-end text recognition in natural images is a challenging problem that has received much attention recently. Traditional systems in this area have relied on elaborate models incorporating carefully hand-engineered features or large amounts of prior knowledge. In this paper, we take a different route and combine the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows us to use a common framework to train highly-accurate text detector and character recognizer modules. Then, using only simple off-the-shelf methods, we integrate these two modules into a full end-to-end, lexicon-driven, scene text recognition system that achieves state-of-the-art performance on standard benchmarks, namely Street View Text and ICDAR 2003.

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